1. What is Big Data?
‘Big Data‘ refers to large volumes of data that traditional systems are unable to handle or process effectively. It may be structured, semi-structured, or unstructured. It will power AI, automation, and business intelligence by 2026.
2. What are the 5 Vs of Big Data?
The 5 Vs of Big Data include Volume, Velocity, Variety, Veracity, and Value. Explain storage, movement, classification, validation, and utilization of data. Other firms also have additional Vs, such as Variability and Visualizations.
3. Why is Big Data important in 2026?
Data-driven real-time decisions are needed by organizations within the next five years. The use of AI models is based on Big Data, processes are automated, experiences are personalized, and predictive analytics is driven by Big Data. It ensures business competitiveness.
4. What is Hadoop?
Hadoop is a free distributed storage and processing system. It consists of HDFS, YARN, and MapReduce as its basic building blocks. Despite the loss in popularity, it is still able to serve an adequate number of legacy systems.
5. What is HDFS?
HDFS, or Hadoop Distributed File System, is a system that stores large files across numerous nodes. It remains healthy through network data replication. HDFS provides scalability for huge datasets.
6. What is MapReduce?
MapReduce is a parallel programming model for processing large datasets. It consists of two phases Map, which filters and sorts, and Reduce, which aggregates results. MapReduce is slower than in-memory engines such as Spark.
7. What is Apache Spark?
Spark is a distributed data-processing framework that uses in-memory calculation. It provides support to batch jobs, streaming, machine learning, and graph processing. As of 2026, it is still the most popular tool for Big Data.
8. Spark vs Hadoop MapReduce – Difference?
Spark is memory-based, which provides it with a speed advantage. Hadoop MapReduce pushes intermediate data to disk, which introduces latency. Spark also supports more workloads than batch processing.
9. What are RDDs in Spark?
RDDs, or Resilient Distributed Datasets, are read-only sets of data which are calculated concurrently. They provide finer adjustment but have to be manually adjusted. Mostly, nowadays, Data Frames are used.
10. What is a Data Frame in Spark?
A Data Frame is a distributed table-like structure of data that has columns with names. It uses the Catalyst Optimizer to carry out efficient execution, and it becomes the default API of most Spark workloads.
11. What is Apache Kafka?
Kafka is a streaming and messaging service. It is necessary in event-based architectures and manage high-volume, real-time data ingestion.
12. What is a Kafka Topic?
A topic is a logical pathway for publishing messages. It may be split into partitions that allow several consumers to read simultaneously and make the pipeline scalable.
13. What is a Data Lake?
A Data Lake is used to store raw and unprocessed data in its natural form. It is compatible with AI, analytics, and machine learning. WSW tools commonly used are AWS S3, ADLS, and GCS.
14. What is a Data Warehouse?
A data warehouse is a store of structured and edited information to be used with analytics and reporting. It is based on schema-on-write. Snowflake and BigQuery are the most famous solutions.
15. What is a Lake house Architecture?
A lake house is a combination of the dynamic nature of a data lake and the performance of a data warehouse. ACID semantics and time travel are offered by tools like Delta Lake, Iceberg, and Hudi. It is the most popular architecture to use in 2026.
16. What is ETL?
ETL is an abbreviation that means Extract, Transform, and Load. Information is initially mined, processed, and finally loaded into the database. Conventional ETL pipelines are capable of working with structured data, whereas contemporary pipelines can rely on ELT in a cloud-based setting.
17. What is ELT?
ELT is the acronym of Extract, Load, and Transform. Under this method, the data is loaded to the warehouse without the occurrence of any transformation. It takes advantage of the robust engine of cloud computing. The applications of ELT can be found in Snowflake and BigQuery.
18. What is Schema-on-Read?
In Schema-on-Read, data is not stored with the schema, but only the schema is applied once the data is queried. This gives it more flexibility in processing unstructured data and is typical in data lakes, where raw data is stored.
19. What is Schema-on-Write?
In the case of Schema-on-write, the schema is defined prior to the data being written. This guarantees the quality of data and a well-organized format of data storage, which is optimal for data warehouses.
20. What is Apache Flink?
Flink is an engine of real-time streaming. It is low-latency event-time processing. Flink is popular with important real-time applications.
21. What does real-time data processing mean?
Data processing is done in real time. It is mostly applied to fraud detection, the Internet of Things appliances, and real-time dashboards. The most popular are Apache Kafka, Apache Flink, and Spark streaming.
22. What is batch processing?
Data processing Batch processes data in fixed segments and then performs processing. It is best suited to creating reports, conducting historical analyses, and creating data pipes. Both Spark and Hadoop contribute to the idea of batch workflow.
23. What is Data Partitioning?
Data partitioning splits data into small, manageable segments according to attribute values like date or area. This method enhances functionality and allows parallelism. It has widespread application in Spark and data warehouses.
24. What is Data Replication?
The process of data replication makes copies of data in different clusters to improve reliability. It ensures fault tolerance and high availability. Replication is used in systems such as HDFS and Kafka.
25. What is Data Sharding?
In data sharding, data is partitioned horizontally, and the data is distributed to different nodes. This has the effect of evenly distributing workloads along the infrastructure. It is a common practice in NoSQL databases and other distributed systems.
26. What does NoSQL stand for?
‘NoSQL’ refers to non-relational, non-scalable databases. The major ones are key-value store, column store, graph database, and document store. The most common ones are Apache Cassandra and MongoDB.
27. What is Apache Cassandra?
Apache Cassandra is a NoSQL database characterized by high scalability and high write throughput. It has been built to be highly available and is commonly used with large-scale real-time data workloads, which are characterized by high-volume and high-velocity ingestion of data.
28. What is MongoDB?
MongoDB is a non-relational database that is document-oriented. The data is saved in flexible and JSON-like documentation, and it is therefore a good decision when developing an application that requires rapid updates and is semi-structured.
29. What is Delta Lake?
Delta Lake is an open-source storage layer that offers ACID transactions for data lakes. Other functionalities, including time travel, schema evolution, and data compacting, have led to its quick adoption in lakehouse architecture.
30. What is Apache Iceberg?
Apache Iceberg is a table format with high performance and is designed to handle big data workloads. It has native support of ACID, schema evolution, and advanced partitioning, and it is used by leading cloud providers like Snowflake and AWS.
31. What is Apache Hudi?
Apache Hudi is a format of a data lake table that supports incremental processing. It allows upserts and can delete effectively, and is good with streaming data lakes.
32. What is Data Governance?
Data governance refers to the control of the availability, confidentiality, and quality of data. It provides adherence and develops trust. Unity Catalog and Apache Atlas are some of the tools used to enforce governance policies.
33. What is Data Lineage?
Data lineage is a record of the flow of data as it moves to the destination. This monitoring contributes to pipeline auditing and debugging and is also needed in controlled environments.
34. What is a DAG?
A Directed Acyclic Graph (DAG) shows the sequence of the activities in a pipeline. In a scheduler, e.g., Airflow or Spark, DAGs are used to run tasks in a sequence.
35. What is Apache Airflow?
Apache Airflow is a coordination system used to run pipelines. It is a Python-written software that is favored by ETL and batch flows.
36. What is MLOps?
MLOps refers to the operationalization of machine learning models in a production process. MLOps is versioned, automated, and monitored, and closely associated with Big Data systems.
37. What is Serverless Computing?
Serverless computing is a cloud architecture that has auto-scaling functionality, i.e.. You do not have to maintain the servers. Big Data utilizes serverless computing as a model driven by events. Cases of serverless computing include AWS Lambda and Azure Functions.
38. What is Snowflake?
Snowflake is a cloud-based data warehouse designed with elastic compute. It supports SQL, Machine Learning, and semi-structured data. It is popular for analytics with less operational overhead.
39. What is Google BigQuery?
Google BigQuery is a serverless data warehouse from Google. It is recognized for its ability to run fast SQL queries on your data in large datasets. Google BigQuery supports Business Intelligence, Machine Learning, and federated queries.
40. What is Azure Synapse?
A united analytic platform that incorporates SQL, Spark, and pipelines. It facilitates data engineering and warehousing tasks. It is deeply rooted within Azure services.
41. What is Data Skew?
An uneven distribution of data across partitions leads to slow tasks and performance issues. This typically requires re-partitioning or salting.
42. What is a Data Pipeline?
A series of actions that transport and transform data. It includes data ingestion, processing, and loading. Examples of these tools include Kafka, Airflow, and Spark.
43. What is DataOps?
DataOps is an approach that supports automated and optimized data pipelines. It emphasizes quality, continuous integration and delivery, and monitoring. DataOps gives teams the ability to deliver reliable data faster.
44. What is a Distributed System?
A distributed system consists of components that run on different nodes while functioning as one. This enables the system not only to be fault-tolerant but also to scale rapidly. Distributed systems are the foundation of Big Data.
45. What is Fault Tolerance?
Fault tolerance is the ability of a system to function in the presence of component failure. This is often achieved with the use of replication and distributed architecture. It is critical in Big Data contexts.
46. What is Data Serialization?
The process of transforming data into a certain format that can be written to storage or sent somewhere. Common formats include Avro, Parquet, and ORC. Data serialization is used extensively in distributed systems.
47. What is Parquet?
A columnar storage format optimized for analytical workloads. Parquet files offer improved compression and read performance. It has become the standard for lake house storage.
48. What is Avro?
A row-based storage format that includes schemas. Avro is often used for data exchange and as the serialization format for Kafka messages. Avro supports schema evolution, meaning it can accommodate changes to its schema.
49. What is YARN?
Yet Another Resource Negotiator (YARN) is considered the cluster manager in Hadoop. Its job is to allocate compute resources and capabilities to applications. Most popular as part of legacy cluster technology.
50. What are the skills needed for a Big Data Engineer in 2026?
You will want to have a working knowledge of Spark, Kafka, Cloud, SQL, Python, Delta Lake, and DataOps. Ideally, you would have an understanding of the lake house architecture. Real-time streaming and knowledge of integrating ML will be a bonus.
